Job Description
Our client, a multinational telecom technology company, is looking for two Lead GenAI / AI-ML SME's to drive technical strategy on a high-visibility agentic AI program. This is a chance to architect solutions that sit at the intersection of large language models and real-world network operations, with direct influence on how the company's infrastructure is monitored, diagnosed, and improved.
Why This Role Matters:
You will not just implement AI, you will define how it works. As the go-to SME for a network anomaly detection initiative, you will guide technical direction, mentor engineering teams, and see your architecture decisions deployed at scale. This is a high-impact, high-visibility position for someone who wants ownership over cutting-edge GenAI systems rather than a narrow slice of one.
What You Will Do:
* Serve as the AI/ML SME for a network anomaly agentic AI program, shaping solution design and implementation strategy from the ground up
* Design and build LLM-powered agents and multi-agent workflows for anomaly detection, triage, root-cause investigation, and operational insight generation
* Develop RAG capabilities that connect network data, documentation, incident history, and knowledge bases into a unified intelligence layer
* Lead the technical playbook for prompt engineering, tool-calling, agent routing, structured outputs, and evaluation frameworks that keep AI-assisted operations reliable and trustworthy
* Partner with architects, engineers, data teams, product owners, and client stakeholders to translate business and network operations needs into working AI capabilities
* Drive code reviews, architecture discussions, and sprint delivery while sharing knowledge across the broader GenAI engineering team
Required Skills
Required:
- Strong Python software engineering experience with hands-on GenAI, AI/ML, or LLM-based application delivery.
- Experience with LangChain, LangGraph, or equivalent agentic AI frameworks.
- Strong understanding of LLM APIs, tool calling, prompt engineering, structured outputs, streaming, and agent orchestration patterns.
- Experience designing RAG solutions using embeddings, vector search, hybrid retrieval, re-ranking, and answer generation.
- Ability to operate as a technical SME with strong communication, consulting, solution design, and stakeholder-facing skills.
- Working knowledge of cloud AI services, APIs, databases, observability, testing, and production deployment practices.
Preferred:
- Experience with network operations, anomaly detection, incident analysis, telecommunications, or enterprise operations use cases preferred.
- AWS, Azure, or Google Cloud AI/ML certification preferred.
- Relevant certification or training in artificial intelligence, machine learning, data engineering, cloud architecture, or software engineering preferred.
- Network, telecom, or operations-focused certifications are a plus.
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